# This implementation is based on the DenseNet-BC implementation in torchvision
# https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py

import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from collections import OrderedDict
from pytorch.net.se_module import SELayer
import numpy as np

def _bn_function_factory(norm, relu, conv):
    def bn_function(*inputs):
        concated_features = torch.cat(inputs, 1)
        bottleneck_output = conv(relu(norm(concated_features)))
        return bottleneck_output

    return bn_function


class _DenseLayer(nn.Module):
    def __init__(self, num_input_features, growth_rate, bn_size, drop_rate, efficient=False):
        super(_DenseLayer, self).__init__()
        self.add_module('norm1', nn.BatchNorm2d(num_input_features)),
        self.add_module('relu1', nn.ReLU(inplace=True)),
        self.add_module('conv1', nn.Conv2d(num_input_features, bn_size *
                        growth_rate, kernel_size=1, stride=1, bias=False)),
        self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)),
        self.add_module('relu2', nn.ReLU(inplace=True)),
        self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate,
                        kernel_size=3, stride=1, padding=1, bias=False)),
        self.drop_rate = drop_rate
        self.efficient = efficient

    def forward(self, *prev_features):
        bn_function = _bn_function_factory(self.norm1, self.relu1, self.conv1)
        if self.efficient and any(prev_feature.requires_grad for prev_feature in prev_features):
            bottleneck_output = cp.checkpoint(bn_function, *prev_features)
        else:
            bottleneck_output = bn_function(*prev_features)
        new_features = self.conv2(self.relu2(self.norm2(bottleneck_output)))
        if self.drop_rate > 0:
            new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)
        return new_features


class _Transition(nn.Sequential):
    def __init__(self, num_input_features, num_output_features):
        super(_Transition, self).__init__()
        self.add_module('norm', nn.BatchNorm2d(num_input_features))
        self.add_module('relu', nn.ReLU(inplace=True))
        self.add_module('conv', nn.Conv2d(num_input_features, num_output_features,
                                          kernel_size=1, stride=1, bias=False))
        self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))


class _DenseBlock(nn.Module):
    def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate, efficient=False):
        super(_DenseBlock, self).__init__()
        for i in range(num_layers):
            layer = _DenseLayer(
                num_input_features + i * growth_rate,
                growth_rate=growth_rate,
                bn_size=bn_size,
                drop_rate=drop_rate,
                efficient=efficient,
            )
            self.add_module('denselayer%d' % (i + 1), layer)

    def forward(self, init_features):
        features = [init_features]
        for name, layer in self.named_children():
            new_features = layer(*features)
            features.append(new_features)
        return torch.cat(features, 1)

#######################################################################################################
########   DenseNet  #########
#######################################################################################################

class DenseNet(nn.Module):
    r"""Densenet-BC model class, based on
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
    Args:
        growth_rate (int) - how many filters to add each layer (`k` in paper)
        block_config (list of 3 or 4 ints) - how many layers in each pooling block
        num_init_features (int) - the number of filters to learn in the first convolution layer
        bn_size (int) - multiplicative factor for number of bottle neck layers
            (i.e. bn_size * k features in the bottleneck layer)
        drop_rate (float) - dropout rate after each dense layer
        num_classes (int) - number of classification classes
        small_inputs (bool) - set to True if images are 32x32. Otherwise assumes images are larger.
        efficient (bool) - set to True to use checkpointing. Much more memory efficient, but slower.
    """
    # def __init__(self, growth_rate=12, block_config=(16, 16, 16), compression=0.5,
    #              num_init_features=24, bn_size=4, drop_rate=0,
    #              num_classes=10, small_inputs=True, efficient=False):
    def __init__(self, growth_rate=12, block_config=(16, 16, 16), compression=0.5,
                 num_init_features=24, bn_size=4, drop_rate=0,
                 num_classes=10, small_inputs=True, gvp_out_size = 1, efficient=False):

        super(DenseNet, self).__init__()
        assert 0 < compression <= 1, 'compression of densenet should be between 0 and 1'
        # 原来的代码是用平均池化来计算的，这样显存就需要很大
        # 我改成 全局的平均池化
        self.gvp_out_size = gvp_out_size

        # First convolution
        if small_inputs:
            self.features = nn.Sequential(OrderedDict([
                ('conv0', nn.Conv2d(3, num_init_features, kernel_size=3, stride=1, padding=1, bias=False)),
            ]))
        else:
            self.features = nn.Sequential(OrderedDict([
                ('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)),
            ]))
            self.features.add_module('norm0', nn.BatchNorm2d(num_init_features))
            self.features.add_module('relu0', nn.ReLU(inplace=True))
            self.features.add_module('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1,
                                                           ceil_mode=False))

        # Each denseblock
        num_features = num_init_features
        for i, num_layers in enumerate(block_config):
            block = _DenseBlock(
                num_layers=num_layers,
                num_input_features=num_features,
                bn_size=bn_size,
                growth_rate=growth_rate,
                drop_rate=drop_rate,
                efficient=efficient,
            )
            self.features.add_module('denseblock%d' % (i + 1), block)
            num_features = num_features + num_layers * growth_rate
            if i != len(block_config) - 1:
                trans = _Transition(num_input_features=num_features,
                                    num_output_features=int(num_features * compression))
                self.features.add_module('transition%d' % (i + 1), trans)
                num_features = int(num_features * compression)

        # Final batch norm
        self.features.add_module('norm_final', nn.BatchNorm2d(num_features))

        # global avg pool
        self.pool_final = nn.Sequential(OrderedDict([
            ("relu_final", nn.ReLU(inplace=True)),
            ('gap_final', nn.AdaptiveAvgPool2d(self.gvp_out_size))
            ]))

        # Linear layer
        self.feature_dim =  num_features * np.sum(self.gvp_out_size)
        self.classifier = nn.Linear(self.feature_dim, num_classes)

        # Initialization
        for name, param in self.named_parameters():
            if 'conv' in name and 'weight' in name:
                n = param.size(0) * param.size(2) * param.size(3)
                param.data.normal_().mul_(math.sqrt(2. / n))
            elif 'norm' in name and 'weight' in name:
                param.data.fill_(1)
            elif 'norm' in name and 'bias' in name:
                param.data.fill_(0)
            elif 'classifier' in name and 'bias' in name:
                param.data.fill_(0)

    def forward(self, x):
        features = self.features(x)
        pool_feature = self.pool_final(features)
        self.out_feature = pool_feature.view(pool_feature.size(0), -1)
        out = self.classifier(self.out_feature)
        return out


class _PoolBlock(nn.Module):
    def __init__(self, gvp_out_size):
        super(_PoolBlock, self).__init__()
        # self.gvp_out_size = gvp_out_size
        self.relu_final = nn.ReLU(inplace=True)
        self.aap_final = nn.AdaptiveAvgPool2d(gvp_out_size)
        self.amp_final = nn.AdaptiveMaxPool2d(gvp_out_size)
        # self.drop_final = nn.Dropout2d(p=0.5)

    def forward(self, x):
        r = self.relu_final(x)
        ap_result = self.aap_final(r)
        mp_result = self.amp_final(r)
        apmp_final = torch.cat([ap_result, mp_result], 1)
        # pool_feature = self.drop_final(apmp_final)
        pool_feature = apmp_final
        return pool_feature

class ExtendedDenseNet(nn.Module):
    r"""Densenet-BC model class, based on
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
    Args:
        growth_rate (int) - how many filters to add each layer (`k` in paper)
        block_config (list of 3 or 4 ints) - how many layers in each pooling block
        num_init_features (int) - the number of filters to learn in the first convolution layer
        bn_size (int) - multiplicative factor for number of bottle neck layers
            (i.e. bn_size * k features in the bottleneck layer)
        drop_rate (float) - dropout rate after each dense layer
        num_classes (int) - number of classification classes
        small_inputs (bool) - set to True if images are 32x32. Otherwise assumes images are larger.
        efficient (bool) - set to True to use checkpointing. Much more memory efficient, but slower.
    """
    def __init__(self, growth_rate=12, block_config=(16, 16, 16), compression=0.5,
                 num_init_features=24, bn_size=4, drop_rate=0,
                 num_classes=10, gvp_out_size = 1, efficient=False):

        super(ExtendedDenseNet, self).__init__()
        assert 0 < compression <= 1, 'compression of densenet should be between 0 and 1'
        # 原来的代码是用平均池化来计算的，这样显存就需要很大
        # 我改成 全局的平均池化
        self.gvp_out_size = gvp_out_size

        # First convolution
        self.features = nn.Sequential(OrderedDict([
            ('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)),
        ]))
        self.features.add_module('norm0', nn.BatchNorm2d(num_init_features))
        self.features.add_module('relu0', nn.ReLU(inplace=True))
        self.features.add_module('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1,
                                                       ceil_mode=False))

        # Each denseblock
        num_features = num_init_features
        for i, num_layers in enumerate(block_config):
            block = _DenseBlock(
                num_layers=num_layers,
                num_input_features=num_features,
                bn_size=bn_size,
                growth_rate=growth_rate,
                drop_rate=drop_rate,
                efficient=efficient,
            )
            self.features.add_module('denseblock%d' % (i + 1), block)
            num_features = num_features + num_layers * growth_rate
            if i != len(block_config) - 1:
                trans = _Transition(num_input_features=num_features,
                                    num_output_features=int(num_features * compression))
                self.features.add_module('transition%d' % (i + 1), trans)
                num_features = int(num_features * compression)

        # Final batch norm
        self.features.add_module('norm_final', nn.BatchNorm2d(num_features))

        # global pool
        self.pool_final = _PoolBlock(self.gvp_out_size)

        # Linear layer
        self.feature_dim = 2 * num_features * np.sum(self.gvp_out_size)
        self.classifier = nn.Linear(self.feature_dim, num_classes)

        # Initialization
        for name, param in self.named_parameters():
            if 'conv' in name and 'weight' in name:
                n = param.size(0) * param.size(2) * param.size(3)
                param.data.normal_().mul_(math.sqrt(2. / n))
            elif 'norm' in name and 'weight' in name:
                param.data.fill_(1)
            elif 'norm' in name and 'bias' in name:
                param.data.fill_(0)
            elif 'classifier' in name and 'bias' in name:
                param.data.fill_(0)

    def forward(self, x):
        features = self.features(x)
        pool_feature = self.pool_final(features)

        self.out_feature = pool_feature.view(pool_feature.size(0), -1)
        out = self.classifier(self.out_feature)
        return out

# Double Magnification combination (DMC)
class DMC_DenseNet(ExtendedDenseNet):
    def __init__(self, growth_rate=12, block_config=(16, 16, 16), compression=0.5,
                 num_init_features=24, bn_size=4, drop_rate=0,
                 num_classes=10, gvp_out_size = 1, efficient=False):

        super(DMC_DenseNet, self).__init__(growth_rate, block_config, compression,
                                           num_init_features, bn_size, drop_rate,
                                           num_classes, gvp_out_size, efficient)

        # Linear layer
        self.DSC_feature_dim = 2 * self.feature_dim
        self.DSC_classifier = nn.Linear(self.DSC_feature_dim, num_classes)

    def forward(self, x20, x40):
        x = torch.cat((x20, x40), 0)
        features = self.features(x)
        pool_feature = self.pool_final(features)
        out_feature_mix = pool_feature.view(pool_feature.size(0), -1)
        out_feature20, out_feature40 = out_feature_mix.chunk(2, 0)

        out20 = self.classifier(out_feature20)
        out40 = self.classifier(out_feature40)

        self.out_feature = torch.cat([out_feature20, out_feature40], 1)
        out = self.DSC_classifier(self.out_feature)
        return out20, out40, out


#######################################################################################################
########   SE-DenseNet  #########
#######################################################################################################
class SEDenseNet(nn.Module):
    r"""Densenet-BC model class, based on
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_

    Args:
        growth_rate (int) - how many filters to add each layer (`k` in paper)
        block_config (list of 4 ints) - how many layers in each pooling block
        num_init_features (int) - the number of filters to learn in the first convolution layer
        bn_size (int) - multiplicative factor for number of bottle neck layers
          (i.e. bn_size * k features in the bottleneck layer)
        drop_rate (float) - dropout rate after each dense layer
        num_classes (int) - number of classification classes
    """

    def __init__(self, growth_rate=12, block_config=(6, 12, 24, 16),
                 num_init_features=24, bn_size=4, drop_rate=0, num_classes=2, gvp_out_size=1):

        super(SEDenseNet, self).__init__()

        self.gvp_out_size = gvp_out_size

        if isinstance(num_classes, int):
            self.MultiTask = False
        else:
            if len(num_classes) > 1:
                self.MultiTask = True
                num_classes = num_classes[0]
                num_classes2 = num_classes[1]
            else:
                self.MultiTask = False
                num_classes = num_classes[0]

        # First convolution
        self.features = nn.Sequential(OrderedDict([
            ('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)),
            ('norm0', nn.BatchNorm2d(num_init_features)),
            ('relu0', nn.ReLU(inplace=True)),
            ('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),
        ]))

        # # Add SELayer at first convolution
        # self.features.add_module("SELayer_0a", SELayer(channel=num_init_features))

        # Each denseblock
        num_features = num_init_features
        for i, num_layers in enumerate(block_config):
            # Add a SELayer
            self.features.add_module("SELayer_%da" % (i + 1), SELayer(channel=num_features))

            block = _DenseBlock(num_layers=num_layers, num_input_features=num_features,
                                bn_size=bn_size, growth_rate=growth_rate, drop_rate=drop_rate)
            self.features.add_module('denseblock%d' % (i + 1), block)

            num_features = num_features + num_layers * growth_rate

            if i != len(block_config) - 1:
                # Add a SELayer behind each transition block
                self.features.add_module("SELayer_%db" % (i + 1), SELayer(channel=num_features))

                trans = _Transition(num_input_features=num_features, num_output_features=num_features // 2)
                self.features.add_module('transition%d' % (i + 1), trans)
                num_features = num_features // 2

        # Final batch norm
        self.features.add_module('norm5', nn.BatchNorm2d(num_features))

        # Add SELayer
        self.features.add_module("SELayer_0b", SELayer(channel=num_features))

        # global avg pool
        self.pool_final = nn.Sequential(OrderedDict([
            ("relu_final", nn.ReLU(inplace=True)),
            ('gap_final', nn.AdaptiveAvgPool2d(self.gvp_out_size))
            ]))

        # Linear layer
        self.feature_dim = num_features * np.sum(self.gvp_out_size)
        if self.MultiTask:
            self.out = nn.ModuleList()
            self.classifier = nn.Linear(num_features * np.sum(self.gvp_out_size), num_classes)
            self.classifier2 = nn.Linear(num_features * np.sum(self.gvp_out_size), num_classes2)
            self.out.append(self.classifier)
            self.out.append(self.classifier2)
        else:
            self.classifier = nn.Linear(num_features * np.sum(self.gvp_out_size), num_classes)

        # Official init from torch repo.
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.constant_(m.bias, 0)

    def forward(self, x):
        features = self.features(x)
        output = self.pool_final(features)
        output = output.view(features.size(0), -1)

        if self.MultiTask:
            # output = F.relu(features, inplace=True)
            # output = F.avg_pool2d(output, kernel_size=self.avgpool_size).view(features.size(0), -1)
            out1 = self.out[0](output)
            out2 = self.out[1](output)
            return out1, out2
        else:
            # out = F.relu(features, inplace=True)
            # out = F.avg_pool2d(out, kernel_size=self.avgpool_size).view(features.size(0), -1)
            out = self.classifier(output)
            return out
